Optimization techniques and inverse problems: Probing of acoustic impedance profiles in time domain

1982 ◽  
Vol 72 (4) ◽  
pp. 1276-1284 ◽  
Author(s):  
D. Lesselier
Keyword(s):  
Inverse Problems ◽  
Time Domain ◽  
Acoustic Impedance ◽  
Optimization Techniques

An Alternative Design Approach for Vehicle Crash Safety Using Crash Pulse Optimization

2000 ◽  
Author(s):  
R. J. Yang ◽  
C. H. Tho ◽  
C. C. Gearhart ◽  
Y. Fu
Keyword(s):  
Time Domain ◽  
Numerical Optimization ◽  
Piecewise Linear ◽  
Linear Representation ◽  
Optimization Techniques ◽  
Safety Requirements ◽  
Crash Safety ◽  
Design Variables ◽  
Piecewise Linear Representation ◽  
Occupant Injury

Abstract This paper presents an approach, based on numerical optimization techniques, to identify an ideal (5 star) crash pulse and generate a band of acceptable crash pulses surrounding that ideal pulse. This band can be used by engineers to quickly determine whether a design will satisfy government and corporate safety requirements, and whether the design will satisfy the requirements for a 5 star crash rating. A piecewise linear representation of the crash pulse with two plateaus is employed for its conceptual simplicity and because such a pulse has been shown to be sufficient for reproducing occupant injury behavior when used as input into MADYMO models. The piecewise linear crash pulse is parameterized with 7 design variables (5 for time domain and 2 for acceleration domain) in the optimization process. A series of sample runs are conducted to validate that pulses falling within the acceptable crash pulse band do in fact satisfy 5 star requirements.

Strategies for solving inverse problems in thermal processes and systems

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yogesh Jaluria
Keyword(s):  
Experimental Data ◽  
Inverse Problems ◽  
Boundary Conditions ◽  
Temperature Variation ◽  
Small Region ◽  
Optimization Techniques ◽  
Thermal Processes ◽  
Thermal Systems ◽  
Content Type ◽  
Final Design

Purpose This paper aims to discuss inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches that may be used to obtain results that lie within a small region of uncertainty. Therefore, the non-uniqueness of the solution is reduced so that the final design and boundary conditions may be determined. Optimization methods that may be used to reduce the uncertainty and to select locations for experimental data and for minimizing the error are presented. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. Design/methodology/approach In most analytical and numerical solutions, the basic equations that describe the process, as well as the relevant and appropriate boundary conditions, are known. The interest lies in obtaining a unique solution that satisfies the equations and boundary conditions. This may be termed as a direct or forward solution. However, there are many problems, particularly in practical systems, where the desired result is known but the conditions needed for achieving it are not known. These are generally known as inverse problems. In manufacturing, for instance, the temperature variation to which a component must be subjected to obtain desired characteristics is prescribed, but the means to achieve this variation are not known. An example of this circumstance is the annealing, tempering or hardening of steel. In such cases, the boundary and initial conditions are not known and must be determined by solving the inverse problem to obtain the desired temperature variation in the component. The solutions, thus, obtained are generally not unique. This is a review paper, which discusses inverse problems that arise in a variety of practical thermal processes and systems. It presents some of the approaches or strategies that may be used to obtain results that lie within a small region of uncertainty. It is important to realize that the solution is not unique, and this non-uniqueness must be reduced so that the final design and boundary conditions may be determined with acceptable accuracy and repeatability. Optimization techniques are often used for minimizing the error. This review presents several methods that may be applied to reduce the uncertainty and to select locations for experimental data for the best results. A few examples of thermal systems are given to illustrate the applicability of these methods and the challenges that must be addressed in solving inverse problems. By considering a variety of systems, the paper also shows the importance of solving inverse problems to obtain results that may be used to model and design thermal processes and systems. Findings The solution of inverse problems, which frequently arise in thermal processes, is discussed. Different strategies to obtain the conditions that lead to the desired result are given. The goal of these approaches is to reduce uncertainty and obtain essentially unique solutions for different circumstances. The error of the method can be checked against known conditions to see if it is acceptable for the given problem. Several examples are given to illustrate the use of these methods. Originality/value The basic strategies presented here for solving inverse problems that arise in thermal processes and systems, as well as the optimization techniques used to reduce the domain of uncertainty, are fairly original. They are used for certain challenging problems that have not been considered in detail earlier. Several methods are outlined for considering different types of problems.

scholarly journals Feature Optimization for Gait Phase Estimation with a Genetic Algorithm and Bayesian Optimization

2021 ◽  
Vol 11 (19) ◽  
pp. 8940
Author(s):  
Wonseok Choi ◽  
Wonseok Yang ◽  
Jaeyoung Na ◽  
Giuk Lee ◽  
Woochul Nam
Keyword(s):  
Genetic Algorithm ◽  
Time Domain ◽  
Time Window ◽  
Optimal Parameter ◽  
Estimation Error ◽  
Optimization Techniques ◽  
Bayesian Optimization ◽  
Series Data ◽  
Phase Estimation ◽  
Gait Phase

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.

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